首页|A novel graph oversampling framework for node classification in class-imbalanced graphs

A novel graph oversampling framework for node classification in class-imbalanced graphs

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Graph neural network(GNN)is a promising method to analyze graphs.Most existing GNNs adopt the class-balanced assumption,which cannot deal with class-imbalanced graphs well.The oversampling technique is effective in alleviating class-imbalanced problems.However,most graph oversampling methods generate synthetic minority nodes and their edges after applying GNNs.They ignore the problem that the representations of the original and synthetic minority nodes are dominated by majority nodes caused by aggregating neighbor information through GNN before oversampling.In this paper,we propose a novel graph oversampling framework,termed distribution alignment-based oversampling for node classification in class-imbalanced graphs(named Graph-DAO).Our framework generates synthetic minority nodes before GNN to avoid the dominance of majority nodes caused by message passing in GNNs.Additionally,we introduce a distribution alignment method based on the sum-product network to learn more information about minority nodes.To our best knowledge,it is the first to use the sum-product network to solve the class-imbalanced problem in node classification.A large number of experiments on four real datasets show that our method achieves the optimal results on the node classification task for class-imbalanced graphs.

graph neural networkclass-imbalanced graphssum-product networkoversamplingnode clas-sification

Riting XIA、Chunxu ZHANG、Yan ZHANG、Xueyan LIU、Bo YANG

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Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China

College of Artificial Intelligence,Jilin University,Changchun 130012,China

College of Computer Science and Technology,Jilin University,Changchun 130012,China

College of Communication Engineering,Jilin University,Changchun 130012,China

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National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central Universities,JLU

2021ZD0112500U22A2098621721856220220062206105

2024

10.1007/s11432-023-3897-2

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(6)
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